System and method to enable detection of viral infection by users of electronic communication devices

ABSTRACT

A non-transitory computer readable medium that stores instructions for causing a computerized system to perform the following operations: determining, by the computerized system, that a first person is infected by a first infectious disease; wherein the determination is associated with a first person infection probability attribute; detecting, by the computerized system, based upon location information collected during at least a portion of a first infectious disease manifestation period, the location information being indicative of locations of the first person and other persons, a second person that was within an infection distance from the first person and is potentially infected by the first infectious disease; calculating, by the computerized system, a second person infection probability attribute; and updating, by the computerized system, the first person infection probability attribute in response to the second person infection probability attribute.

RELATED APPLICATIONS

This application claims the priority of U.S. provisional patent Ser. No.61/561,884 filing date Nov. 20, 2012, which is incorporated herein byreference.

FIELD OF THE INVENTION

The invention relates generally to viral infection, and, moreparticularly, to a system and method to enable detection of viralinfection by users of electronic communication devices.

BACKGROUND OF THE INVENTION

An infection is the colonization of a host organism by parasite species.Infecting parasites seek to use the host's resources to reproduce, oftenresulting in disease. Colloquially, infections are usually considered tobe caused by microscopic organisms or microparasites like viruses,prions, bacteria, and viroids, though larger organisms likemacroparasites and fungi can also infect.

Hosts normally fight infections themselves via their immune system.Mammalian hosts react to infections with an innate response, ofteninvolving inflammation, followed by an adaptive response.Pharmaceuticals can also help fight infections.

In order for infecting organisms to survive and repeat the cycle ofinfection in other hosts, they (or their progeny) must leave an existingreservoir and cause infection elsewhere. Transmission of infections cantake place via many potential routes. Infectious organisms may betransmitted either by direct or indirect contact. Direct contact occurswhen an individual comes into contact with the reservoir. This may meantouching infected bodily fluids or drinking contaminated water or beingbitten by the deer tick. Direct contact infections can also result frominhalation of infectious organisms found in aerosol particles emitted bysneezing or coughing. Another common means of direct contacttransmission involves sexual activity—oral, vaginal, or anal sex.

Indirect contact occurs when the organism is able to withstand the harshenvironment outside the host for long periods of time and still remaininfective when specific opportunity arises. Inanimate objects that arefrequently contaminated include toys, furniture, door knobs, tissuewipes or personal care products from an infected individual. Consumingfood products and fluid which have been contaminated by contact with aninfecting organism is another case of disease transmission by indirectcontact.

The main public health danger is a rapidly spreading, highly pathogenicairborne (respiratory) infection reminiscent of the 1918 Spanish flu orthe 2003 SARS pandemic. These viruses are airborne and can thus rapidlyinfect large groups of people.

SUMMARY OF THE INVENTION

According to an embodiment of the invention a method may be provided andmay include performing any of the stages illustrated in thespecification and/or drawings.

Further embodiments of the invention include a computer readable mediumthat is non-transitory and may store instructions for performing theabove-described methods and any steps thereof, including anycombinations of same. For example, the computer readable medium maystore instructions for executing, by a computerized system, any of thestages illustrated in the specification and/or drawings. Any referenceto a method should be interpreted as including a reference to anon-transitory computer readable medium that stores instructions forexecuting the method by a computerized system. Any reference to anon-transitory computer readable medium should be interpreted asreferring to a method that is executing according to the instructionsstored at the non-transitory computer readable medium.

The computerized system includes at least one hardware component.

Additional embodiments of the invention include a computerized systemarranged to execute any or all of the methods described above, includingany stages—and any combinations of same.

There may be provided, according to an embodiment of the invention, anon-transitory computer readable medium that may store instructions forcausing a computerized system to perform the following operations:determining, by the computerized system, that a first person is infectedby a first infectious disease; wherein the determination is associatedwith a first person infection probability attribute; detecting, by thecomputerized system, based upon location information collected during atleast a portion of a first infectious disease manifestation period, thelocation information being indicative of locations of the first personand other persons, a second person that was within an infection distancefrom the first person and is potentially infected by the firstinfectious disease; calculating, by the computerized system, a secondperson infection probability attribute; and updating, by thecomputerized system, the first person infection probability attribute inresponse to the second person infection probability attribute.

The determining may be responsive to at least one of (a) first personmedical information fed by the first person, and (b) clinicalinformation related to at least one of the first person and the firstinfectious disease.

The determining may be responsive to signatures of multiple infectiousdiseases.

The location information may be collected from hand held devices of thepersons.

The location information may be collected from short-range transmissiondevices carried by the persons.

The location information may be collected from long-range transmissiondevices carried by the persons.

The determining and the calculating are responsive to demographicinformation relating to the first and second persons and to ademographic element of the signature of the first infectious disease.

The computer readable medium may store instructions for updating, by thecomputerized system, the first person infection probability attributeand the second person infection probability attribute in response toclinical symptoms information relating to at least one of the firstperson, second person and the first infectious disease.

There may be provided, according to an embodiment of the invention, anon-transitory computer readable medium that may store instructions forcausing a computerized system to perform the following operations:receiving, by a computerized system, (a) location information relatingto locations of multiple persons within a certain period of time; (b)clinical symptoms information related to one or more out of (a) at leastone person of the multiple persons or (b) at least one of the locations;and evaluating, by the computerized system, probabilities of infectionsof the multiple persons by at least one infectious disease, in responseto the location information and to the clinical symptoms information.

The non-transitory computer readable medium may store instructions forgenerating a person infection alert if a probability of infection of theperson by an infectious disease exceeds a first threshold; and fortransmitting the person infection alert to at least the person.

The non-transitory computer readable medium that may store instructionsfor generating a geographical zone infection alert if probabilities ofat least a predefined number of persons that were, during the certainperiod of time, within the geographical infection alert exceed a secondthreshold; and for transmitting the geographical zone infection alert toa plurality of persons.

The non-transitory computer readable medium that may store instructionsfor calculating infectious disease signatures in response to thelocation information and to the clinical symptoms information.

The non-transitory computer readable medium that may store instructionsfor receiving by the computerized system, medical information providedby the persons; and wherein the evaluating, by the computerized system,of the probabilities of infections of the multiple persons may befurther responsive to medical information provided by the persons.

The non-transitory computer readable medium that may store instructionsfor receiving by the computerized system, medical information sensed bymobile devices of the multiple persons; and wherein the evaluating, bythe computerized system, of the probabilities of infections of themultiple persons may be further responsive to medical informationprovided by the persons.

The non-transitory computer readable medium that may store instructionsfor receiving voice information sensed by mobile devices of the multiplepersons; and processing the voice information to detect disease relatedvoices.

The non-transitory computer readable medium that may store instructionsfor calculating, by the computerized system, infections diseasetransmission pathways based upon the location information and theclinical symptoms information.

There may be provided, according to an embodiment of the invention, anon-transitory computer readable medium that may store instructions forcausing a computerized system to perform the following operations:receiving, by a computerized system,

(a) location information relating to locations of multiple persons atdifferent points of time; and (b) medical information that may includeat least one of (i) medical information fed by at least one person; (ii)medical information sensed by at least one mobile device of at least oneperson; (iii) clinical symptoms information related to at least oneperson; and (iv) medical symptoms information related to at least one ofthe locations; andcalculating by the computerized system and in response to the locationinformation and to the medical information, infectious diseasessignatures that comprise spatial and temporal characteristics of adistribution of the infections diseases.

The non-transitory computer readable medium that may store instructionsfor evaluating, by the computerized system, probabilities of infectionsof the multiple persons by at least one infectious disease, in responseto the location information and to the, infectious disease signatures.

The non-transitory computer readable medium that may store instructionsfor receiving by the computerized system demographic information relatedto at least one of the locations; and calculating by the computerizedsystem and in response to the location information, the medicalinformation and the demographic information, infectious diseasessignatures that may include environmental condition, spatial andtemporal characteristics of the distribution of the infections diseases.

The non-transitory computer readable medium that may store instructionsfor receiving by the computerized system environmental informationrelated to the multiple locations persons; and calculating by thecomputerized system and in response to the location information, themedical information and the demographic information, infectious diseasessignatures that comprise demographic, spatial and temporalcharacteristics of the distribution of the infections diseases.

There may be provided, according to an embodiment of the invention, anon-transitory computer readable medium that may store instructions forcausing a mobile device of a person to perform the following operations:obtaining person medical information, wherein the person medicalinformation is obtained by at least one out of (i) receiving personmedical information from the person and (iii) sensing person medicalinformation; detecting person location information indicative oflocations of the person during multiple points in time; sending theperson location information and the person medical information to acomputerized system; receiving from the computerized system a personinfection alert that is generated in response to person locationinformation gathered from multiple persons and in response to medicalinformation; person medical information from at least one person; andinforming the person that the person is suspected as being infected bythe infectious disease.

There may be provided, according to an embodiment of the invention, anon-transitory computer readable medium that may store instructions forcausing a computerized system to perform the following operations:receiving, by a computerized system, (a) location information relatingto locations of multiple persons within a certain period of time; (b)medical information related to at least one person of the multiplepersons or (b) at least one of the locations; and (c) environmentalinformation relating to at least one of the locations; and evaluating,by the computerized system, probabilities of infections of the multiplepersons by at least one infectious disease, in response to the locationinformation, to the medical information and to the environmentalinformation.

There may be provided, according to an embodiment of the invention, amethod to be executed by a computerized system, the method may include:determining, by the computerized system, that a first person is infectedby a first infectious disease; wherein the determination is associatedwith a first person infection probability attribute; detecting, by thecomputerized system, based upon location information collected during atleast a portion of a first infectious disease manifestation period andindicative of locations of the first persons and other persons, a secondperson that was within an infection distance from the first person andis potentially infected by the first infectious disease; calculating, bythe computerized system, a second person infection probabilityattribute; and updating, by the computerized system, the first personinfection probability attribute in response to the second personinfection probability attribute.

There may be provided, according to an embodiment of the invention, amethod to be executed by a computerized system, the method may include:receiving, by a computerized system, (a) location information relatingto locations of multiple persons within a certain period of time; (b)clinical symptoms information related to one or more out of (a) at leastone person of the multiple persons or (b) at least one of the locations;and evaluating, by the computerized system, probabilities of infectionsof the multiple persons by at least one infectious disease, in responseto the location information and to the clinical symptoms information.

There may be provided, according to an embodiment of the invention, amethod to be executed by a computerized system, the method may include:receiving, by a computerized system, (a) location information relatingto locations of multiple persons at different points of time; and (b)medical information that may include at least one of (i) medicalinformation fed by at least one person; (ii) medical information sensedby at least one mobile device of at least one person; (iii) clinicalsymptoms information related to at least one person; and (iv) medicalsymptoms information related to at least one of the locations; andcalculating by the computerized system and in response to the locationinformation and to the medical information, infectious diseasessignatures that comprise spatial and temporal characteristics of adistribution of the infections diseases.

There may be provided, according to an embodiment of the invention, amethod to be executed by a mobile device of a user, the method mayinclude: obtaining person medical information, wherein the personmedical information is obtained by at least one out of (i) receivingperson medical information from the person and (iii) sensing personmedical information; detecting person location information indicative oflocations of the person during multiple points in time; sending theperson location information and the person medical information to acomputerized system; receiving from the computerized system a personinfection alert that is generated in response to person locationinformation gathered from multiple persons and in response to medicalinformation; person medical information from at least one person; andinforming the person that the person is suspected as being infected bythe infectious disease

There may be provided, according to an embodiment of the invention, amethod to be executed by a computerized system, the method may includereceiving, by a computerized system, (a) location information relatingto locations of multiple persons within a certain period of time; (b)medical information related to at least one person of the multiplepersons or (b) at least one of the locations; and (c) environmentalinformation relating to at least one of the locations; and evaluating,by the computerized system, probabilities of infections of the multiplepersons by at least one infectious disease, in response to the locationinformation, to the medical information and to the environmentalinformation.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to understand the invention and to see how it may be carriedout in practice, a preferred embodiment will now be described, by way ofa non-limiting example only, with reference to the accompanyingdrawings, in the drawings:

FIG. 1 is a schematic drawing of the “viral strategy,” constructedaccording to the principles of the present invention;

FIG. 2 is a flow chart illustration of the collection and analysis ofpopulation-level information, constructed according to the principles ofthe present invention;

FIG. 3 is a schematic drawing of the creation of a “viral ecologicalsystem,” constructed according to the principles of the presentinvention;

FIG. 4 is a schematic drawing of the disease transmission model,constructed according to the principles of the present invention;

FIG. 5 is a schematic drawing of a seeded infection simulation,constructed according to the principles of the present invention;

FIG. 6 is a schematic drawing of a “productive interaction,” leading toa an infection event, constructed according to the principles of thepresent invention;

FIG. 7 is a schematic drawing of how infected users recover and developimmunity (or die), constructed according to the principles of thepresent invention;

FIG. 8 is a schematic drawing of how an activity profile is used to“track” disease, constructed according to the principles of the presentinvention;

FIG. 9 is a flow chart illustration of the collection of userparameters, constructed according to the principles of the presentinvention;

FIG. 10 illustrates a method according to an embodiment of theinvention;

FIG. 11 illustrates a method according to an embodiment of theinvention;

FIG. 12 illustrates a method according to an embodiment of theinvention;

FIG. 13 illustrates a method according to an embodiment of theinvention;

FIG. 14 illustrates a method according to an embodiment of theinvention;

FIG. 15 illustrates a method according to an embodiment of theinvention;

FIG. 16 illustrates a method according to an embodiment of theinvention;

FIG. 17 illustrates a method according to an embodiment of theinvention;

FIG. 18 illustrates a method according to an embodiment of theinvention; and

FIG. 19 illustrates a computerized system and its environment accordingto an embodiment of the invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

It will be appreciated that for simplicity and clarity of illustration,elements shown in the figures have not necessarily been drawn to scale.For example, the dimensions of some of the elements may be exaggeratedrelative to other elements for clarity. Further, where consideredappropriate, reference numerals may be repeated among the figures toindicate corresponding or analogous elements.

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the invention.However, it will be understood by those skilled in the art that thepresent invention may be practiced without these specific details. Inother instances, well-known methods, procedures, and components have notbeen described in detail so as not to obscure the present invention.

The subject matter regarded as the invention is particularly pointed outand distinctly claimed in the concluding portion of the specification.The invention, however, both as to organization and method of operation,together with objects, features, and advantages thereof, may best beunderstood by reference to the following detailed description when readwith the accompanying drawings.

It will be appreciated that for simplicity and clarity of illustration,elements shown in the figures have not necessarily been drawn to scale.For example, the dimensions of some of the elements may be exaggeratedrelative to other elements for clarity. Further, where consideredappropriate, reference numerals may be repeated among the figures toindicate corresponding or analogous elements.

Because the illustrated embodiments of the present invention may for themost part, be implemented using electronic components and circuits knownto those skilled in the art, details will not be explained in anygreater extent than that considered necessary as illustrated above, forthe understanding and appreciation of the underlying concepts of thepresent invention and in order not to obfuscate or distract from theteachings of the present invention.

Any reference in the specification to a method should be applied mutatismutandis to a system capable of executing the method and should beapplied mutatis mutandis to a non-transitory computer readable mediumthat stores instructions that once executed by a computer result in theexecution of the method.

Any reference in the specification to a system should be applied mutatismutandis to a method that can be executed by the system and should beapplied mutatis mutandis to a non-transitory computer readable mediumthat stores instructions that can be executed by the system.

Any reference in the specification to a non-transitory computer readablemedium should be applied mutatis mutandis to a system capable ofexecuting the instructions stored in the non-transitory computerreadable medium and should be applied mutatis mutandis to method thatcan be executed by a computer that reads the instructions stored in thenon-transitory computer readable medium.

The principles and operation of a method and an apparatus according tothe present invention may be better understood with reference to thedrawings and the accompanying description, it being understood thatthese drawings are given for illustrative purposes only and are notmeant to be limiting.

Accordingly, it is a principle object of the present invention, based onmeta-analysis of information gathered from the provider (e.g., AT&T),the device and the user of the application himself to provide anindication to a mobile device (e.g. smartphone) user of vicinities inwhich he may be at increased risk of contracting viral infections andalso provide information regarding other quality of life regardingenvironmental phenomena (air pollution, noise, and due to the presencein that area of an increased number of viral disease carrying ‘suspects’etc. due to the presence in that area of an increased number of viraldisease carrying ‘suspects’) due to the presence in that area of anincreased number of viral disease carrying ‘suspects’ due to thepresence in that area of an increased number of viral disease carrying‘suspects’.

It is another principle object of the present invention, similarlybased, to provide users with bio social feedback, using parametersgathered from the device it is possible to choose a profile from a listof predefined ‘activity-profiles’ (e.g., active, lazy, social, anxiousetc.). The aim of the biosocial feedback is to help the user retain (oravoid) chosen activity-profiles for self-improvement and other purposes(support groups, challenge games on individual/group level etc.).

There are two primary methods utilized by the application in achievingthe above aims:

Information about the General Population:

Indication of the activity level of the general population in thatvicinity (e.g., all mobile users, all subscribers of a certain carrier,all users of a certain smartphone operating system). The level ofactivity can be derived from statistical attributes of the mobiledevices present in that area. Statistical attributes can include motionas determined from the communication signals (GPS, cell tower, receptionsignal variance) and can be calibrated against the background of‘normal’ behavior per a specific mobile device or per the “population”of mobile devices present in that specific area. This data requirescooperation from the providers. Also, the user must then “subscribe” tothese sort of alerts.

General indications of the presence/absence of a large number ofdevice-carrying individuals at specific types of establishments, e.g. aspike in the presence of individuals at local health centers (clinics)or absence from “normal” places such as “work place” or “gym” atspecific periods. The workplace of an individual user may be inferred,for example by patterns of early travel to and late travel from the samelocation, Mondays through Fridays. Data may be correlated with seasons,weather, environmental data and national holidays.

Specific, local indications of the well-being or activity-profile ofapplication users (or others that enable data collection). Well-beingand/or activity-profile in general will be determined by the followingparameters:

Any of the information available from the device's sensors (microphone,GPS, illumination sensor, vibration sensor, inclinometer, accelerometer,motion sensor, reception signal and camera) as well as any other sensorsthat may be added in the future to sample the users environment andprovide information. In particular, information could be used to analyzethe user's relative mobility or to identify specific indicators of viralsymptoms (e.g. cough, changes in the user's voice). The information canbe collected on a regular basis; following specific triggers (e.g.certain changes in behavior patterns, general indications of diseaseprevalence); or with active participation of the user (e.g. recording ofcough, sensing of breathing etc.)

Data otherwise provided that can be linked to specific mobile devicessuch as on social networking sites or in other databases, which theapplication has access to, such as Google flu-trends.

Direct indications provided by users about their well-being. These couldbe self-initiated or in response to a prompt by the application(periodic, seasonal or due to suspicious patterns in the locality orrelated localities). Data may include subjective feeling of user,specific measurements preformed by device (e.g. breathing pattern),external measurements (e.g. body temperature) or expert opinion (e.g.diagnosis by a medical doctor) or indications of activity (e.g., metfriends for dinner, missed a day at work for no reason, couldn't get up,etc.).

Specific information about the mobile device previous locations, such ashealthcare facilities, which may correlate to a higher likelihood ofinfection.

The relevant ‘vicinity’ could refer to the immediate environment (e.g.office, restaurant), general geographic location (e.g. city) or usersnetwork environment (e.g. friends from users phonebook or user-definedfriends or family) to provide an estimate of general disease andspecifically lung/upper airways-related infections and other generalenvironmental qualities (air quality, noise levels, etc.)

Another potential use of the application is to collect statisticallysignificant data that would enable academic, disease control studiesproviding insights into the mechanisms of viral transmission and viralinfection, both in general and for specific strains and even guidepublic-health measures.

The application may have various levels of privacy settings, controllingwhich information may be shared with others.

The application may be ‘stand alone’ or an added feature on anotherapplication (e.g., a mobile social networking application).

The application may be sold or distributed for free and may enabletargeted advertising. Statistical information may be provided, with orwithout charge, to research institutions and commercial companies.

The application is not limited to viral/bacterial infections only. Itmay provide indications of other environmental phenomena (such as airquality) or used to identify other factors impacting individualwell-being (e.g. asthma, heart condition). The camera associated withthe user's smartphone could be used effectively to detect strap throat

The application's primarily aim is to provide an indication to a mobiledevice (e.g. smartphone) user of vicinities in which he may be atincreased risk of contacting viral infections due to the presence inthat area of an increased number of viral disease carrying ‘suspects.’

There are two primary methods for determining presence of such suspects:

Information about the General Population:

Indication of the activity level of the general population in thatvicinity (e.g., all mobile users, all subscribers of a certain carrier,all users of a certain smartphone operating system) are noted. The levelof activity can be derived from statistical attributes of the mobiledevices present in that area. Statistical attribute can include motionas determined from the communication signals (GPS, cell tower, receptionsignal variance) and can be calibrated Vs. ‘normal’ behavior per aspecific mobile device or per mobile devices present in that specificarea.

General indications of the likelihood of presence of a large number ofviral carrying suspects at a specific type of establishment (e.g.fitness center in specific periods) are noted.

Indication of the well-being of users of other device present in thesame vicinity who are also users of the application or otherwise enabledata collection which may indicate their well being. The well-beingcould be determined by:

Any of the information available from the device's sensors (microphone,GPS, illumination sensor, vibration sensor, inclinometer, accelerometer,motion sensor, reception signal and camera) as well as any other sensorsthat may be added in the future to sample the users environment andprovide. In general information could be used to analyze the user'srelative mobility or to identify specific indicators of viral symptoms(e.g. cough). The information can be collected on a regular basis;following specific triggers (e.g. certain changes in behavior patterns,general indications of disease prevalence); or with active participationof the user (e.g. recording of cough, sensing of breathing, etc, . . .).

Data otherwise provided that can be linked to specific mobile devicessuch as on social networking sites or in other data bases which theapplication has access to.

Indications provided by users of their well being. These could be selfinitiated or in response to a prompt by the application (periodic or dueto suspicious patterns)

Information about the mobile device previous locations, such ashealthcare facilities may correlate to a higher likelihood of infection.

The relevant ‘vicinity’ could refer to use of the user's smartphonemicrophone, GPS and camera to sample the users environment, whether itis the immediate environment (e.g. office, restaurant, user himself),local environment (e.g. room), general geographic location (e.g. city)or users network environment (e.g. friends from users phonebook) toprovide an estimate of general disease and specifically lung/upperairways-related infections and other general environmental qualities(air quality, noise levels, etc.)

Another aim of the application is to collect statistically significantdata that would enable academic, drug-development, and disease controlstudies into the mechanisms of viral infection, both in general and forspecific strains.

The application may have various levels of privacy setting controllingwhich information may be shared with others.

The application may be ‘stand alone’ or an added feature on anotherapplication (e.g. a mobile social networking application).

The application may be sold or distributed for free and may enabletargeted advertising. Statistical information may be provided to with orwithout charge to research institutions and commercial companies.

The application is not limited to viral infections only. It may provideindications of other phenomena (such as air quality) or used to identifyother factors impacting individual behavior (e.g. heart conditions).

The system and method may be adapted to:

Provide user with a basic indication of a potential viral infection ofthe user's lung, in accordance with FDA approval and liability issues;

Provide user with a basic indication of a potential viral infection ofthe user's upper airways/throat

Provide user with a basic indication of a potential viral infection inthe user's general geographic vicinity network-wise, that is, inconnected friends like contact from users' phonebook

Provide user with a basic indication of a potential viral infection inthe user's general geographic vicinity geography-wise, that is, theamount of infections in a given geographic area (e.g., Palo Alto,Calif.)

Provide user with a basic indication of a potential viral infection inthe user's specific geographic vicinity (e.g., the restaurant you havejust entered), that is, in the area the user is present at

Provide indication of air-quality in the users any geographic vicinity(e.g., smoke-filled area, level of micro-particles) and at various timesduring the day/week, where information is available (provided there areenough users of the application in the vicinity)

Provide indication of noise levels in any geographic vicinity (e.g.,background noise levels, specific noises such as car horns) and atvarious times during the day/week, where information is available(provided there are enough users of the application in the vicinity)

Provide user with tools to perform a basic diagnosis of friends andrelatives (e.g., children)—application will NOT replace an expert'sevaluation, in accordance with any necessary FDA approvals.

Provide user with tools to perform a medical grade evaluation of selfand relatives using specific hardware described below (“Smartoscope”)

Provide health authorities with statistical data on symptom distributionin the population on a geographic basis

Provide user with tools to follow his own current behavior pattern asdefined by application and compare to his “normal self” pattern. App maythen attempt to define “abnormal states” and help user correct ifnecessary. For example if depression is detected (e.g. through reductionin movement, changes in movement pattern, excessive sleep, subjectivereport of bad mood) the app may suggest immediate steps to take (kind oflike a personal eTrainer . . . )

Indicate to user how his pattern differs from his normal self and ifnecessary guide user BACK into normal pattern in a sort ofsocial/behavior-level bio social feedback

Provide user with tools to follow his own current behavior pattern asdefined by application and compare to the “Average population” patternfor fun or other purposes

Allow user to define goals (e.g. “I want to be a bit more active”) andprovide feedback when goals are not met. This will be done using theparameters used by the app. For example the app may suggest going tovisit the Gym again (“We have detected a drop at your exerciseprofile—are you feeling well? Could use a visit to the gym?” etc.)

“User states” may be defined using ALL parameters that may be monitoredon the device such as specific tests designed for the application, theapps used, time spent using apps, user performance on these applicationsassuming they produce a result, time spent on phone, ratio of usertalking to “other” (other person talking, silence) while on the phone,user velocity, user movement pattern etc. (?). The app may also ask oraccept user specific input on parameters such as “Worked 2 extra hoursevery day”, “spoke to my ex-girlfriend”, “went to the gym” etc. UsingGPS or specific info (use access to phonebook/SMS) the app may thenallow user to keep track of these event without the user participation.

User state will be defined based on all or some of these parameters.User state is a dynamic application and may be either based onpre-defined profiles in the application (“social”, “mobile”, “active”,“stressed”, “focused” etc.) OR defined by the user as a combination ofthe pre-defined profiles (e.g., 30% active, 70% focused) or entirely bythe user. After a state has been defined the app will update user on hisprogress in obtaining this state (i.e., biosocial feedback)

User may choose to SHARE states and add them to the app as “publicuser-defined states”. User may add his name or nickname to these states(e.g., “The Galmogy state”). Only friends of user will see these statesand may spread them (share) to their friends as well. Successful states(i.e., states that have been shared by over X users) may win prizes foruser (crowd-sourcing)

User may CHALLENGE friends to “compete” in achieving these states(either user or pre-defined)

Users may form “groups” for any of the purposes above, whereinnon-private group information will be shared with user for motivation,gaming or other purposes.

User may PUBLISH his results in his circle of friends or globally.Published results will never contain any private information unlessspecified so by user. That is, user may publish to his friends(contacts) or a subset of his friends that he is “100% focused” (i.e.,achieving the goal of being 100% focused from the pre-defined states) or“60% stressed” but may also publish specific details such as “X has been90% mobile this week, covering a distance of 230 km in 3 days”

User may publish results from aim 1-9 anonymously or not, in order toprovide “health warnings” or “updates” to friends not using theapplication. Results will be published to specific contacts (fromphonebook), all contacts or the general public of users. Thesepublications may include warnings with various levels of specificity(e.g., “The application has detected a large spike in influenza likeillness in Haifa/east palo-alto”etc.) or updates such as “X is no longercoughing and back to full capacity—mazal tov!”

User may “subscribe” to news from various areas, i.e., receivenon-private updates from areas defined by user (e.g., if user subscribesto palo-alto for “disease related info” he will receive a summary ofupdates from users in that area). Only updates defined by the user as“public” will be shared with users outside of the user's contact list.Private information will never be shared publicly. With regard to allaspects of the application, private information such as actual soundbites recorded, specific measurements etc. will only be stored on theLOCAL device and discarded after relevant data has been extracted.

Methods:

The application (app) is based on data collected by service providers(AT&T etc.) and the intrinsic microphone, GPS, camera and all otheraccessible devices of the smartphone (phonebook, list of calls(incoming/outgoing/missed), SMS etc. and will operate on both theindividual and the population level for comparison purposes (e.g., howmuch do I talk compared to the average). Private data will never beshared unless specified so by user.

Category 1: Creating Baseline Sick or Activity-Profiles

When first turned ‘on’, the app creates a baseline profile for each userdefined by a set of parameters collected from the device. Each machinemay contain profiles for multiple people as defined by user (e.g., me,my wife, my co-worker Jim etc.) Since only one person actually carriesthe device it is not possible to use some parameters, such as movementfor all profiles. The parameters used to create the baseline profileinclude data collected from the device built-in modules (Microphone,camera, GPS, etc.) as well as from specifically designed or generalexternal modules (thermometer, barometer, hygrometer or stato scope-likemicrophones for hand-held devices (smartoscope described in Appendix1)). This baseline profile will be updated regularly with user consentand will require the user to be in a “normal” state when it is created.Over time the application can create an “average” baseline and definethe standard deviation for each parameter recorded and for the parameterset as a whole. All the data collected will be stored by default onlylocally on the device itself to prevent privacy issues.

Similarly, the user will be encouraged to notify the app when “feelingsick” (or other situations such as: vacation, anxious, social, worketc.) to allow the app to create a defined parameter activity-profilefor these “states”. It may also become more specific and divide “states”into levels, e.g. “sick” into light vs. severe, stomach vs. airwaysetc., “anxious” into light stress, heavy stress or even types of stress(work, social etc.)

For the user activity-profiles it creates the application will monitorsound (built-in microphone), movement (GPS/cell tower, inclinometer,vibrations), picture and illumination (Built-in camera). Soundmonitoring includes parameters such as speech, background noise,heart-rate, sound of air going through the upper/lower airways, cough,sneeze, pitch of voice when talking time spent on phone, usingapplications etc. Movement monitoring includes total distance traveled(per hour or part of it, per day), speed, and movement pattern of theuser, vibrations, angle at which device is held when active etc. Thecamera can be used to monitor the eye and throat (color of tonsils,white dots on throat indicative of bacterial infection) as well asgeneral appearance (fallen/sunken face etc.). Information from externaldevices may be fed in manually—body temperature, blood pressure, heartrate, subjective feeling, number of hours and quality of sleep etc.

Application Monitoring Modes

After creating these statistical baseline activity-profiles, the appwill allow users to choose a surveillance level between active testingand full sentinel modes. In active testing mode (highest privacysetting) the app will NOT run in the background and will only “monitor”when the user activates it for a particular purpose—for example todefine a “disease state”, check the color of the user's throat (not easyby eye), measure any of the other parameters or even for checking how“normal” his/her behavior has been on that day/week, compared to the“normal” baseline.

In full sentinel mode (most “socially-aware” mode) the app will run inthe background and periodically “sample” or “monitor” the user and theenvironment for the parameters used in the user's profile. The level ofmonitoring is determined by privacy settings, so there are a fewpossible modes, not just “0 and 1.”

Thus in this mode the app can refine over time its definitions for whatis a “Normal profile” and how the “normal environment” sounds like. Whenthe normal profile changes significantly enough (e.g. outside 2 STD) theapp will alert user that is has detected a potential “change-of-state”and prompt user to indicate what is the cause of this change in observedpattern, e.g. if a disease-like activity-profile has been detected itwill prompt: “We have noticed a significant change in your behaviorpattern and cough—are you feeling well?” If the answer is that the useris feeling well it will adjust the normal profile and/or try to find adifferent state that matches the particular activity-profile detected.If the user indeed does not feel well it will prompt further actionssuch as measuring temperature, taking pictures of throat, usingmicrophone as a statoscope (see Appendix 1 below) etc. In the case ofthe application used as a semi-diagnostic tool, at the end of theanalysis process it will offer the user its non-medical opinion and askfor user feedback to improve the analysis. Feedback will be either fromuser subjective feeling (symptoms have passed after 2 days and I'mfeeling better) or from medical expert (I was diagnosed with flu-likeillness). If user was indeed feeling sick or diagnosed as sick by theprofessional it will define the pattern observed as a “strong diseasestate”. If this disease state is detected again it will suggest going toclinic for further expert diagnosis by a prompt saying: “last time youdisplayed this pattern it was diagnosed by the physician as a case ofthe Flu—it is likely your symptoms indicate a flu-like illness”. Thesame holds for other states defined by the other activity-profiles.

In both active and sentinel modes and with user permission, private datawill be stored locally on the device while non-private summary of datawill be sent to a secure database where all consenting users non-privateprofile will be used for scientific pandemic evaluations on thepopulation level, defining population-level activity-profiles (i.e., anaverage “anxious state” profile etc.) and research. Users will havevarying levels of privacy settings to choose from.

The most extreme privacy setting would allow no transmission ofinformation about the user at all, i.e. only reception of alerts foruser vicinity, but the system and method may most likely limit the dataprovided to such users. The user will also have an option to allow hisdata to be used for improvements to the application and scientificresearch.

NOTE that statistical data from all users may be used to improve thedefinition of “states” on the individual level; e.g., data from theuser's environment such as number of identified disease cases in thesame geographic region will be used as a “population-level” parameterused for epidemic-potential evaluation, i.e., using both user data asdescribed above and adding to that the prevalence of disease ingeographic and network vicinity in providing an opinion/feedback.

Application Environmental Warnings and Population-Level Data

Data contributed by ALL users that are willing to share non-privateinformation will be used to define “normal” vs. “abnormal” environments(i.e., high proportion of infections, noise, pollution, active peopleetc.). For example, if many users in a particular location (e.g. a city)display individual signs of flu-like illness at a given time-point thenthe system will mark that city accordingly and notify users that thearea displays high frequency of disease cases.

Similarly, when going to an event (music festival) user can check inadvance whether the area displays signs of high concentration of sickpeople, if it has high levels of noise (currently or historically, seeAppendix 2), are the users in the area displaying a “social”activity-profile etc. The system uses two independent methods to definethese local conditions: the amount of users in the area displaying aparticular activity profile (e.g., “sick”) AND data contributed by“sentinel users” that provides insights into noise levels and otherparameters monitored. Note that sentinel users will have the option toallow the app to monitor the environment by sound. It is clear that aphone listening-in on an emergency room during the winter will contain alot of wheezing, sneezing and coughing sounds. Thus it will be marked as“unhealthy.” The app will also treat “network friends” as anenvironment.

Air-quality may be monitored in a similar fashion; the app may be usedto monitor air quality in specific areas far more accurately (higherresolution) in a similar fashion to the one described above, byaggregating the amount of non-sick cough in specific areas. For example,if a substance that causes cough and sneezing (pollen, smoke) isabundant in a small geographic area (using GPS/phone location) then theapp will detect a lot of non-sick coughing/sneezing and mark the area as“potential bad air quality area.”

In an optional embodiment the microphone is used to diagnose airways:

Using the built-in microphone or a special “Smartoscope” (a microphonedesigned to mimic a stato scope) users may be able to diagnose withgreater accuracy the condition of airways for themselves or familymembers such as infants. The app will prompt user to start test andgraphically show user what actions to take, e.g. hold microphone atshown angle against this area for this time, breath in, breath out,cough etc.

The application (app) is based on the intrinsic microphone, GPS andcamera of the smartphone and has two modes:

-   -   Self-diagnosis    -   Environmental (neighborhood) warnings.

All the above and other characteristics and advantages of the inventionwill be further understood through the following illustrative andnon-limitative description of preferred embodiments thereof.

FIG. 1 is a schematic drawing of the “viral strategy,” constructedaccording to the principles of the present invention. Modeling basicviral dynamics requires a “viral strategy.” The main public healthdanger is a rapidly spreading, highly pathogenic airborne (respiratory)infection reminiscent of the 1918 Spanish flu or the 2003 SARS pandemic.These viruses are airborne and can thus rapidly infect large groups ofpeople. Infection is transmitted from Infected (I) individuals 120 viaair/contact to Susceptible(S) 110 individuals. Infected individualsrecover and develop immunity or die, and are Removed from the infectionprocess (R) 130.

FIG. 2 is a flow chart illustration of the collection and analysis ofpopulation-level information, constructed according to the principles ofthe present invention. The system works by collecting and analyzingpopulation-level information Mobile device “raw” information may be usedto create a highly realistic disease transmission model. General coarsemovement information (mobile device tracking services, etc. 210.

Locations of interest visited (e.g., hospitals, clinics 221

Movement pattern 222

Device usage pattern 223

Create a “profile” for each user monitored (no private information).Define “normal”/baseline activity, indicative of a healthy state 230.Compile information from multiple users in a geographic area 240.

FIG. 3 is a schematic drawing of the creation of a “viral ecologicalsystem” from user parameters, constructed according to the principles ofthe present invention. A tracking application 320 is applied to mobileusers 310. A network is constructed from (time-dependentmovement/encounter information 330. A user network at time ‘t’ 340 isillustrated. The dots 341 indicate ‘users,’ the lines 342 indicate ‘usertrajectories.

FIG. 4 is a schematic drawing of the disease transmission model,constructed according to the principles of the present invention. Eachuser (410-470) has an independently profile based on parameterscollected from provider (location, speed).

FIG. 5 is a schematic drawing of a seeded infection simulation,constructed according to the principles of the present invention. A seed(or seeds) is chosen for an infection and the simulation is allowed toprogress. ‘User 7’ 570 is arbitrarily designated as “infected.” Forsimulation purposes one or several users are designated as “infected”The amount of time a user remains infected and the method of infection(e.g., time-spent together in a single location with other users) aredisease-specific and constitute part of the “viral strategy.”

FIG. 6 is a schematic drawing of a “productive interaction,” leading toan infection event, constructed according to the principles of thepresent invention. Interaction is depicted between an infectedindividual 670 and a susceptible individual 620. Using the general dataavailable (location, time spent at various locations) the chain ofresulting infections is determined, where each encounter has aprobability p of effective transmission of the disease.

FIG. 7 is a schematic drawing of how infected users recover and developimmunity (or die), constructed according to the principles of thepresent invention. Infected user 770 has recovered and is now immune, assymbolized by the white coloration. Infected users recover at a ratespecific to the disease and become immune. The infection processcontinues as infected users come into contact with a susceptible user720.

FIG. 8 is a schematic drawing of how a user activity profile is used to“track” disease, constructed according to the principles of the presentinvention. The system “detects” that ‘user 870’ is infected. In thisscenario an attempt is made to track real life infections via usermobility patterns (compared to the normal baseline of each particularuser). FIG. 8 represents an optional embodiment: Historical vs. currentvalues.

When looking at a particular, well-defined geographic area (a publicpark, a neighborhood, an office building) it is often very informativeto look at historical values of parameters measured. This data may bedivided into daily/weekly averages, averages by particular day, time ofday and even correlated to weather conditions. Using these historicalparameters the application can define different activity-profiles forgeographic areas on similar lines as defining activity-profiles forusers, in accordance with privacy issues. That is, an area may bedefined as “quiet,” “loud,” “low-air quality,” “active,” etc., accordingto the parameters provided by application users.

FIG. 9 is a flow chart illustration of the collection of userparameters, constructed according to the principles of the presentinvention. General coarse movement information (broadcast towers, etc.)910; device specific information: GPS, velocimeter, sound, wificonnections, etc. 920; user inputs: subjective feeling, bodytemperature, medical diagnosis, clinical samples, etc. 930 are stored,and automatically define user ‘an activity pattern’ for each user in thesystem 940.

The terms “user” and “person” are used in an interchangeable manner inthis patent application.

According to various embodiments of the invention there is provided anautomatic diagnostic system based on a combination of clinicaldiagnostic information (e.g., MOH publications, National surveys,Hospital data, MD personal reports etc.), environmental factors(weather, allergens, air-pollution), social connectivity structure andpopulation-wide symptom reports (fever, cough, headache, sore throat,diarrhea etc. provided via dedicated smartphone application).

The system may use a transmission logic of infectious disease at thecore of the decision-making process.

That is, infectious disease requires contact between individuals inorder to be transmitted (whether be it direct or indirect contact viasurface/air etc.). Moreover, most seasonal and non-seasonal infectionsappear in individual “peaks” (exact shape determined by localinteraction profile and transmission route, see FIG. 1)—therefore theexact identification of few clinical samples per area at a given timehave significant predictive power for the entire location at aparticular time. This power is increased further when the movements andbasic social connections of patients are added—as may be done using alocation-tracking app and/or connections as they appear in social media(e.g. face book) or as reported by users. Our system is based oncorrelating the clinical samples diagnosed to user symptoms at alocation in a given time, taking into considerations also thedemographics, history and social environment of the user.

To provide the most exact results the system and method may use localclinical data, user reported symptoms and users location patterns toautomatically create exact transmission chains and diagnosis based on“local disease content” (supplied by hospitals, HMOs, MDs, healthorganizations and labs), user location and local and/or global reportedsymptoms at a given time or over a longer period. As a result the systemand method may be able to provide users with particular rather than‘general’ diagnosis (i.e., the system and method may replace ILI(influenza-like-illness) with the exact causing agent of the ILI (e.g.,RSV infection), the system and method may replace “stomach infections”with particular stomach infections (e.g. Salmonella.t) etc.), createdetailed population-wide symptom database for particular disease strainsand even provide users with accurate individual “infection history”showing users what they have been infected with (even when disease issub-clinical) and automatically updated individuals' immunity tables'based on these past infections. Taken together patients will knowexactly what they have been infected with, the course of the infection,how they responded to it, quantify how they responded to differenttreatment options (medication, nutrition, exercise, stress, life-stylechanges etc.), how it compares to the rest of the population and whatimmunity they have acquired.

Furthermore, given our knowledge of disease content in an area, its rateof spread and the demographics it affects the system and method may canprovide users (and/or MDs/Hospitals/HMOs) with accurate individualdisease warnings and the means to avoid them(vaccination/prophylactics/prevention).

There is provided a method (denoted 1000 in FIG. 10) that may include:

-   -   a. Defining (1010) local characteristic per infection, e.g.        length of symptoms persistence per causing agent, range of        symptoms, impact on wellbeing etc.    -   b. Defining (1012) location specific characteristics of a        particular causing agent    -   c. Combining (1014) user symptoms with local clinical data along        with user location history to determine individual most probable        causing agent responsible for the symptoms    -   d. Identifying (1016) unique disease conditions by the        identification of statistical variations (e.g., only one user        has fever in an tire city, accompanied with knee pains→bacterial        infection→emergency trip to the hospital)    -   e. Creating (1018) an ad-hoc “natural vaccination book” for each        user, indicating potential infections user is resistant to or        must be careful of    -   f. Predicting (1020) region to region (e.g., city-to-city)        transmission probability    -   g. Enabling (1022) smart emerging disease detection systems    -   h. Enabling (1024) smart environmental hazard detection systems    -   i. Enabling (1026) smart disease containment strategies    -   j. Enabling (1028) ‘prevention measure success’ quantification        and analysis by time, or weather, or area, or population        demography or any other system parameter relevant to disease        propagation    -   k. Predicting (1030) causing agent based on population wide        symptoms, transmission characteristics, disease spread dynamics        etc.    -   l. Creating (1032) general and specific disease warnings for        users and health organizations, with or without prevention        advice

Infections travel via physical proximity and each causing agent has itsown slightly unique properties of transmission. As a result thetransmission of a particular causing agent (e.g., influenza, salmonella)in a population of a known structure may be predicted with greataccuracy.

The system proposed combines for the first time location supplied by asmartphone application or by any other means (user report, HTML5location, physical presence etc.) and the ‘clinical content’ in adefined geographic region, where clinical content may include of thelaboratory analysis of clinical samples (RT-PCR results, DNA/RNAsequencing, growth on plates etc.), MD diagnostic reports (i.e., thesupposed causing agent as diagnosed by an MD) and reported symptoms(either provided by user himself or medical/health facilities).

The diagnostic process the system and method may define here works inthese stages described in more detail below; for each area and for eachcausing agent a method (denoted 1100 in FIG. 11) may be executed and mayinclude:

-   -   a. Defining (1102) the local causing agent content at time t        based on medical reports and available public and/or private        clinical data (e.g., HMO internal reports, hospital reports, CDC        public info etc.)    -   b. Correlating (1104) clinical symptoms with causing agent        content at given time, by demographics (e.g., causing agent X        causes symptoms Y in social group Z)    -   c. Defining (1106) local symptom distribution in the area        represented by the medical reports using a designated        application (referred to here as “VirusTracker”, VT), reports        from HMOs, or any other means of reporting which are accessible    -   d. Creating (1108) a retrospective and real-time analysis of        outcomes by following users symptoms over time until disease        state is resolved    -   e. Creating (1110) a retrospective and real-time definition of        ‘warning signals’ indicating a disease requires further        attention based on hospital/treatment history    -   f. Clustering (1112) sets of symptoms by demographics and/or        location and/or nature of symptoms and/or retrospective analysis        of outcome (as defined above). This may include automatically        clustering symptoms based on individual variations (age, gender        etc.), locations, time, environmental conditions, individual        responses to disease (complete rest vs. working as usual),        disease progress pattern, distribution of symptoms within a        population, speed of disease spread across and within geographic        areas etc. and ‘back-track’ these symptoms to the most probable        causing agent, based on historical knowledge accumulated in the        system.    -   g. When available, matching (1114) individuals' location and        location history with causing agent to create a past and current        mapping of location of causing agents in given areas    -   h. Creating (1116) potential ‘transmission pathways’ based on        users' location, population density and causing agents in a        given area    -   i. Defining (1118) the ‘density’ of causing agent in the        population from above    -   j. Correlating (1120) causing agent with particular sets of        symptoms—note these sets of symptoms vary between demographic        and other groups (such as infection history of individuals        comprising the groups) and also vary with time/environmental        conditions. E.g., symptoms of influenza infection in a given        area in March may vary from symptoms of the same infection in        December (based on weather, sequence variation, host status        etc.).    -   k. For each diagnosis case, calculating (1122) the probability        of a particular causing agent based on user past location, past        infections (of user), symptoms and the various causing agent        density in the areas visited by user on relevant time-scale        (i.e., time that would match symptoms appearing at time t).    -   l. For each user—calculating (1124) the most probable “symptom        course” (i.e., disease manifestation over time) based on        population-wide symptoms for most probable causing agents and        individual history of user, along with user-reported various        details such as stress-level, exercise, hours of sleep etc.        Stage 1124 may include creating “causing agent signature” based        on the population “disease course” profiles and epidemic spread        dynamics in area where the causing agent is known (via lab        testing etc.)    -   m. Conveying (1126) to a user (or authorized health        organization) the most probable causing agent and/or the likely        disease course (in terms of symptoms) based on the above    -   n. Detecting (1128) statistical anomalies in user symptoms        requiring further medical attention and issue specific warnings        to user and to caretaker/physician defining the nature of the        anomaly    -   o. Continuously updating (1130) disease course definition and        statistical range as well as update causing agent density based        on the addition of clinical data and/or users symptoms.

Stage 1102 may include:

1. Receiving Clinical reports from hospitals. Each report contains thedata in table 1.

ID * Age Gender Date of Date of Result of Fever Symptoms ApproximateTreatment Outcome hospitalization clinical clinical address test test(pos/neg, causing agent)

2. Dividing area into geographic ‘bins’. Size may vary. Bins will beidentified and updated based on geographic disease clusters.

3. For each bin and for each causing agent identified—calculating thenumber of cases, % of causing agent out of total identified cases.

Data is summarized as in table 2. Table 2 includes the followingcolumns:

Bin # # users # positive % positive of % positive of Symptoms totalpositive total (list)

Stage 1104 may include:

1. From table 1 (above)—choosing each demographic group, e.g. 0-1 yearold babies in the east Jerusalem area.

2. For each groups—organizing data by causing agent.

3. For each groups and causing agent pair—identify the distribution ofsymptoms, e.g. fever, throat, vomiting etc.

4. Summarize data as in table 3. Table 3 includes the following columns.

Cluster # Causing Demographic Symptom 1 Symptom 2 Symptom 3 Time (dates)agent group (list) (frequency, range)

Stage 1106 may include:

1. Allowing a mobile application (VT) hosted by a device of a user—toprovide location information, sampling location information when VT isturned on or with every major location change.

2. Allowing users to use VT to fill in basic demographic info atregistration (age, gender, family status, smoking etc.).

3. Allowing user to use VT to fill in basic symptoms (fever, vomiting,headache etc.).

Storing data from the VT reporting symptom and/or from HMOs/local MDs isstored as in table 4. Table 4 includes the following columns:

User/ID Demographic Location Symptom 1 Symptom 2 Symptom 3 Outcome fromgroup (list) (frequency, HMO range)

Stage 1108 may include:

1. Mobile app (VT) allowing users to fill in symptoms daily

2. Gathering (by computerized system) daily data as in table 5 untildisease is resolved.

Table 5 has the following columns:

VT user Demographic Location Date Symptoms Symptoms Symptoms Outcome IDgroup (list) day 1 (list) day 2 (list) day 3 (list) (e.g., etc. tillresolved, disease is medication, resolved hospital etc.)

3. Defining/creating (by computerized system) clusters of “diseasecourse” based on the symptoms, demographics, length of infection, timeand location. That is, similar symptoms from one connected area at atime consistent with infection chain (typically several weeks) will forma “disease course” for an area at a given time (table 6).

Table 6 has the following columns:

Disease Demographic Location Typical Time Typical course group symptoms(dates) outcome group # (list) by day (e.g., (list, resolved, frequency,medication, range) hospital etc.)

4. Table 6 enables assigning new infection cases to a specific clusterbased on location, time, demographics and symptoms.

5. Each cluster is associated with typical length of infection, typicalsymptoms and “complications” (e.g., if user had to go to hospital). Thesystem can share this predictive info with new users reporting symptoms.

Stage 1110 may include:

Part 1—Danger Potential in Terms of Symptoms

1. Collecting (by computerized system) user symptoms via VT app.

2. Assigning (by computerized system) potential causing agent byassigning potential infectious agents to each cluster (pending clinicaldata).

3. Using (by computerized system) the following parameters to asses—the“danger potential” for each group: causing agent, outcome input fromusers in relevant disease course group and hospital outcome data

4. “danger potential” is an arbitrary definition and may be tuned. Forexample, a danger potential may be issued if a reporting user is in agroup which has >X % chance of being hospitalized.

Part 2—Epidemic Potential

1. Collecting (by computerized system) user symptoms via VT app

2. Assigning (by computerized system) potential causing agent assigningpotential infectious agents to each cluster (pending clinical data).

3. Comparing (by computerized system) speed of spread and symptoms fromcurrent epidemic to past data (same causing agent). If pattern and/orspeed of spread and/or severity of symptoms are statistically greaterthan expected the system will issue warning to any relevant body

Stage 1112 may include assigning potential infectious agents to eachcluster (pending clinical data). This may include:

1. Combining (by computerized system) clustered clinical disease contentdata from hospitals (tables 2 and 3) and users disease course groups(table 6) based on location, time, symptoms, demographics and dynamicsof causing agent in the population

2. For a given location/time—identifying (by computerized system)correlations between clusters (hospital) and symptom groups from users

3. Based on the correlations—assigning (by computerized system) to eachdisease course group a “probable causing agent” A correlation betweenclinical data clusters and disease course groups can involve taking intoaccount (i) time when epidemic started in hospital (variousnearby/connected locations) versus time when users started reporting,(ii) Demographics affected in hospital/among app users, and (iii)Typical symptoms and geographic distribution of infected.

Stage 116 may include:

1. Collecting (by computerized system) user symptoms/location via VT.

2. Organizing (by computerized system) on map the time-dependent patternof user reports.

3. Using (by computerized system) users' location history to create atable of epidemiologically related individual users based on “Distance”(table 7).

Table 7 has the following columns:

User ID

Interacting user ID Distance**

Wherein ** “Distance” is a function of physical distance between the twoindividuals (when at least one is infected/reporting symptoms) and thetime spent at a relevant distance

4. Based on the above—attempting (by computerized system) to re-createthe infection chain, where e.g. A user reports symptoms at time=t andwas in proximity to another user at time t+1, after which second userreports symptoms then the “chain” is user 1 infected user 2

Stage 1142 may include:

1. Tracking (by computerized system) the geographic time-course ofsymptoms appearing in the population and define rate of geographicspread as well as ‘disease course’ groups.

3. Attempting (by computerized system) to find a pattern in the localspread, ignoring the noise that comes from entry of infected individualsinto the area (which do not constitute an inherent part of the spread).A pattern consists of geographic spread rate, “disease course” profilesin the population and the relative abundance/order of each profile,e.g., first week we only see profile 1, after that a 50:50 mix ofprofile 1 and 2 etc.

It is noted that these signatures may require a positive identificationof the causing agent in the area.

It is noted that if there are no clinical data the method canextrapolate potential causing agents based on causing agent signature,This may include:

1. Based on population symptoms in a given area at a given time—creating(by computerized system) ‘disease course’ groups as in table 6

2. Use existing disease course groups and the spatial dynamics of theepidemic to try and matching (by computerized system) those with the“causing agent signature” bank on the server (explained above, table 3).

3. Attaching (by computerized system) a “diagnosis” to the reportedsymptoms based on most similar causing agent signature.

Defining Local Disease Content and Symptoms at Time t

The system and method may first create (when possible) a list of causingagents at time t based on clinical reports from health organizations(MDs, HMOs, lab results, hospitals etc.). If there are no such data thesystem and method may extrapolate potential causing agents based on pastdata from the same area and from other regions where such data exists.

A list of causing agents may include of all relevant clinical dataavailable, such as the exact strain and lineage of the causing agent(e.g., Influenza A H3N2, Fuji 2004, RNA sequence of viral geneticcontent etc.), clinical manifestations (headache, fever above 40degrees, diarrhea etc. as well as length and timing of each symptom andsymptom set as a whole), demographics (such as age, gender, location,socio-economic status when available, infection history when availableetc.) and available medical notes.

Creating ‘Symptom-Sets’ Associated with a Particular Causing Agent atTime t.

The system and method may create a ‘manifestation’ table for eachcausing agent divided into clustered symptom sets associated with eachdiscernible group infected, e.g., causing agent X infection inindividuals of group Y is associated with symptom set and clinicaloutcome Z. Each such definition may be static or dynamic in time. Thatis, some causing agents (in some patient groups) may have symptoms thatdo not vary with time. For example, Ebola infection is always associatedwith hemorrhaging to some degree, so the system will define Ebolainfection in ALL groups at ALL times as associated with hemorrhaging.This definition may change if and only if cases of Ebola not associatedwith such phenomena are reported, in which case the system will alsoattempt to define the ‘difference’ that caused the altered symptomsbased on causing agent and/or infected population characteristics. Onthe other hand, some influenza infections lead to sore throat in somepeople while this symptom does not appear in other people, thus thesystem will attempt to find variations in causing agent, individualsinfected and demographic and temporal factors responsible for thedifference in symptoms. When such factors are found (in either of theabove cases) a new infection/symptom group definition is created. Forexample, “Influenza infections in female children under the age of 7 (±2years) during March 2012 are associated with sore throat”.

Above symptom sets may be local or global as the system determines basedon local and global clinical diagnosis and symptom reports.

“Functional mapping” of symptoms to identify causing agent with minimallab testing

The system will also create “functional mapping” of disease outcomesbased on clinical symptom report over time with or without an associatedcausing agent definition. The system will again automatically clustersymptoms based on individual variations (age, gender etc.), locations,time, environmental conditions, individual responses to disease(complete rest vs. working as usual), disease progress pattern,distribution of symptoms within a population, speed of disease spreadacross and within geographic areas etc. and ‘back-track’ these symptomsto the most probable causing agent, based on historical knowledgeaccumulated in the system.

When a cluster of symptoms (cluster in terms of symptoms, demographics,spread dynamics and any of the parameters described above) cannot beassociated with high degree of confidence with any particular causingagent due to any reason the system will create an alert and identify theusers in the local network that are candidates for a positive diseaseidentification via clinical sampling. That is, it will notify any healthorganization, hospital or private company interested that there is adisease pattern in a geographic area (either in real-time orretrospectively) that requires identification for causing agent.

Similarly, if/when a causing agent is identified for a disease thesystem will notify HMOs, hospital etc. that there is no further need forclinical testing at that particular time point for patients displayingsymptoms as defined in the cluster. This recommendation will be based onstatistical confidence and is only a money/labor-saving recommendation.

User Ad-Hoc Vaccination History and Statistical Determination ofCross-Immunity

Users' most likely infection history will be compiled via the VTapplication—after a user reports symptoms the system discovers (inreal-time or retrospectively) the causing agent based on the stepsdescribed above.

The system attaches a “diagnosis” to the reported symptoms (which arekept on the server and/or on the users' device) including date ofinfection (start/end), severity of infection, probable causing agent(strain and specific mutations etc.). Next the system marks the userimmune to specified causing agent and potentially closely relatedagents. To determine closeness the system will use all availablescientific information regarding cross-immunity etc. as wellsystem-generated statistical historical data showing the likelihood ofinfection B given infection A (when available). This way the system willdefine ad-hoc cross-immunities without experimental validation but basedpurely on population-wide infection history.

Note that given this vaccination history, cross-immunity and knowledgeof circulating causing agents the system will update contagionprobabilities for users. Furthermore, the system will be able toaccurately suggest users to get vaccinated or not at a specific time,taking into account their own history, the vaccine strain and thecirculating causing agent.

Also note that in some cases infection history may be verified and/orexplored by consensual antibody analysis of users' blood and/or saliva,where the effectiveness of the user's immune system response againstvarious causing agents will be tested, using standard methods (e.g.,ELISA, HI assay etc.) These data may also be used in order to ‘train’the system and enhance the knowledge database and analytics systems inuse.

Population-Wide Warnings and Epidemic Tracking

The system is constantly attempting to attach symptom clusters tocausing agents based on clinical and historical data. As mentionedabove, the system defines “spread dynamics” to each causing agent, whichserve as a “signature” for an infection, especially when no clinicaldata is available. As such, the system may cluster causing agents by thesymptoms and disease pattern they produce. Using these causing agentclusters the system will automatically detect ‘other’ patterns that donot correspond to any known pattern (or combinations of patterns causedby several causing agents in one location). These ‘other’ patterns willthen be used to define either a novel causing-agent, as in the case ofrapidly emerging viruses such as the H1N1-pn strain, an environmentalcause or a combination of both. As the causing agent is defined forthese new patterns, e.g., via clinical testing, the pattern signaturewill be added to the database so that recurring episodes will be rapidlyidentified.

By using spread pattern signatures the system is able not only toidentify existing diseases but also able to create disease patternpredictions based on the specific causing agent, environmentalcondition, demographics and the movement pattern of VT users. The resultof these predictions for the end user (VT user or customers) is anaccurate spatio-temporal map showing the most likely diseasetrajectories and the time it will take. These predictions will befrequently updated, as the exact individual movements within of thepopulation cannot be predicted but must be taken into account

Based on the above individual VT users will receive specific warningswhen applicable (based on location, demographics, infection and movementhistory). Further, health organization may receive highly efficientvaccination/prevention strategies generated by the system. These willconsist of highly targeted vaccination/prophylactic recommendations(e.g. provide vaccination against pertussis to kindergartens X, Y and Z)as well as other measures (quarantine etc.) if necessary. Note thereporting system features a ‘built-in’ prevention/containment strategyassessment capability—the degree of success of a strategy is reflecteddirectly in the number of new cases in an area. Thus the system may beable to report success/failure in various locations automatically.

Indoor Monitoring of Persons Such as Patients

According to various embodiments of the invention the persons can bemonitored when in door—in buildings, facilities, camps etc.

A hospital-based analysis system for identifying, tracking, containingand preventing hospital acquired infections. The system takes intoaccount clinical test data from the hospital, patient-to-patientcontacts, care giving staff and hospital floor plan, including thelocation of family rooms, WC, food areas etc.

This is an extension of all previous methods presented for thepopulation level diagnosis, albeit applied to the settings of afacility. The system uses person-to-surface (surface may be a table, asyringe or a room etc.) and person-to-person contacts along withclinical test data and/or disease symptoms to create a transmissionpathway and provide predictions on contaminated surfaces locations andpotentially infected individuals.

The system first uses the floor plan to define the facility area.Patients and/or objects (medical equipment, office equipment etc.) maybe marked with RFID tags or by any other device allowing tracking withinthe facility. Each RFID tag is associated with an object or patient ID.Active tracking of these RFID tags allows the system to superimposeobject/individual location history using the ID, locations andtime-stamps for each location. Each location can have a contactcoefficient based on the nature of the location (i.e. WC versus TV room)and/or optimization of system based on past results. That is, the systemmay conclude that a location has a high contact coefficient based on thehigh incidence of transmission observed there.

For each potentially infected (including carrier) individual (or item)the system and method may calculate an epidemic distance (epi-distance)from all other IDs in the system (ID1, ID2, epi-distance) based on theirinteraction history (direct by sharing a location at the same time orindirect via sharing the same item or being in the same location atdifferent times) and the known infections of each ID and/or potentialinfections determined by the system—for example, an ID closelyassociated with a carrier ID may be determined to likely become acarrier, even in the absence of clinical test results.

This embodiment may apply any of the above methods wherein the personsmay be patient and the location information is gathered from a limitedarea.

FIG. 17 illustrates an indoor monitoring method 1700 according to anembodiment of the invention.

Creating (1710) a table of patients containing demographics (age,gender, general address etc.), reason for hospitalization (car accident,internal disease, operation etc.), location in hospital (ward, bed #),treatments received (antibiotics, drugs etc.), treatment/analysis roomsvisited (MRI, CT etc.) and relevant clinical test results (i.e., testsfor infectious agents, e.g. VRE, MRSA etc.). Note that patients may beassigned different “susceptibility” and/or “infectivity” potential tosome or all diseases based on patient data (e.g., demographics).Patients are issued location-tracking devices such as RFID tags orother. Tags are associated with patient file from 1

Processing (1720) location data to create a list of locations visitedincluding ‘time stamps’ containing time entered into location (t_entry)and time leaving the location (t_exit) (Table A).

Table A has the following columns:

Patient ID Location Time (t_entry, t_exit)

Calculating (1730) the epidemiological distance at any given timebetween two users. The epidemiological distance is defined by thepatient-patient and patient-object tables (Tables B and C)—each locationin the facility has a contact coefficient representing the likelihood oftransmission in the area. For example, a kitchen area has higherprobability for transmitting stomach infections than a TV room. Notethese coefficients may be re-calculated as real data accumulates in thesystem by retrofitting parameters. The “Distance” is a value between 0and 1, where a value of 0 is given for patients not in contact directlyor indirectly and 1 indicates strongly related patients.

Table B has the following columns:

Patient ID1 Patient ID2 Duration Location contact Distance coefficient

Table C has the following columns:

Patient ID Object ID Duration Contact coefficient Distance

Creating (1740) a network of patients based on the pair-wise distancesbetween patents. The network is highly dynamic and constantly updatesbased on movement data and contact with surfaces (table D). An exampleto surface contact is visitation to the same examination room forexample.

Table D has the following columns:

Patient ID1 Patient ID2 Distance (direct + indirect)

Adding (1750) clinical data information to the network to establishpotential disease paths as shown in table E (probable route is based on“distance”, pathogen characteristics and patientinfectivity/susceptibility status as defined in 1). Each pathogen hasits own characteristics, such as latency period, transmissionprobability/location (based on transmission route) and objectassociation (i.e., how easily the pathogen can last on surface and whatprobability of infection) as shown in table F.

Table E has the following columns:

Patient ID1 Patient ID2 Distance Transmission probability (diseasestatus) (disease status) (based on patients' data)

Table F has the following columns:

Pathogen Transmission Latency Symptoms Surface contact chance/location

Based on above—creating a summary for patient and object (location)potential status (table G and H).

Table G has the following columns:

Patient ID Causing agent 1 status Causing agent 2 status Etc. (infected,suspected of (infected, suspected of infection (p), suspected infection(p), suspected of being carrier(p)) of being carrier(p))

Table H has the following columns:

Object/area ID Causing Causing Etc. agent 1 status agent 2 status(contaminated (p)) (contaminated (p))

FIG. 18 illustrates method 1800 according to an embodiment of theinvention.

Method 1800 may include: gathering (1810) patient details anddetermining infectivity, susceptibility and risk level; collecting(1820) patient locations (locations, treatments, surface contact whenavailable); determining (1830) patient-patient distance for eachinfection type; determining (1840) potential infection chains withprobabilities (between 0 and 1) and assign patients with status(infected, potential infection (p=), carrier etc; and generating (1850)a report.

Updating Patient Infectivity Data

According to yet another embodiment of the invention the method canincludes defining ad-hoc cross-immunities without experimentalvalidation but based purely on population-wide infection history.

Accordingly, the method may include at least some of the followingstages:

Creating for each user of VT a table of putative infections based onlocation and demographics, reported (based on reported symptoms), andconfirmed (with lab test results and/or MD opinion)

Creating clusters of users showing a similar infection history (ineither putative, reported or confirmed), regardless of location anddemographics

Comparing the three clusters (each based on one column) and looks forgroups of users that were putative but not reported or confirmed,creating a table (table I).

Table I has the following columns:

User ID Putative that did not get reported/confirmed (CA, date)

Calculating for patients in category, for each causing agent average,STD, CA that are statistically underrepresented in confirmed/reportedvs. putative are organized in table J of users and the diseases theyapparently did not contract.

Table J has the following columns:

CA STD from all CA Users “not infected”

For each CA in table J—creating create a table of users in group,including demographics and all (confirmed/reported) CA history

Clustering the users according to CA or a combination of CA. The CA mostassociated with not being infected is a candidate for cross-immunity

Checking likelihood of candidate CAs based on timing of infections—ifthe CA came after the CA by which the table was organized then it IS NOTcausing cross-immunity. If 100% of users had first the candidate CA andthen avoided the putative CA then likelihood is high.

Table K has the following columns:

User ID Demographics CA 1 CA 2

Creating a cross-immunity table (table L) based on results from thechecking of likelihood.

Table L has the following columns:

CA1 CA2 (% immunity)

FIG. 12 illustrates method 1200 according to an embodiment of theinvention.

Method 1200 may include the following sequence of stages: determining(1210), by the computerized system, that a first person is infected by afirst infectious disease; wherein the determination is associated with afirst person infection probability attribute; detecting (1220), by thecomputerized system, based upon location information collected during atleast a portion of a first infectious disease manifestation period andindicative of locations of the first persons and other persons, a secondperson that was within an infection distance from the first person andis potentially infected by the first infectious disease; calculating(1230), by the computerized system, a second person infectionprobability attribute; and updating (1240), by the computerized system,the first person infection probability attribute in response to thesecond person infection probability attribute.

These stages may be repeated for multiple uses and can be executed in aniterative and even recursive manner. In a nut shell—if it is confirmed(for example—by receiving clinical information that confirms that thefirst or second persons are infected by the first infectious diseasethen the probabilities are updated accordingly).

FIG. 13 illustrates method 1300 according to an embodiment of theinvention.

Method 1300 may include the following sequence of stages: receiving(1310), by a computerized system, (a) location information relating tolocations of multiple persons within a certain period of time; (b)clinical symptoms information related to one or more out of (a) at leastone person of the multiple persons or (b) at least one of the locations;and evaluating (1320), by the computerized system, probabilities ofinfections of the multiple persons by at least one infectious disease,in response to the location information and to the clinical symptomsinformation.

FIG. 14 illustrates method 1400 according to an embodiment of theinvention.

Method 1400 may include the following sequence of stages: receiving(1410), by a computerized system, (a) location information relating tolocations of multiple persons at different points of time; and (b)medical information that comprises at least one of (i) medicalinformation fed by at least one person; (ii) medical information sensedby at least one mobile device of at least one person; (iii) clinicalsymptoms information related to at least one person; and (iv) medicalsymptoms information related to at least one of the locations; andcalculating (1420) by the computerized system and in response to thelocation information and to the medical information, infectious diseasessignatures that comprise spatial and temporal characteristics of adistribution of the infections diseases.

FIG. 15 illustrates method 1500 according to an embodiment of theinvention.

Method 1500 may include the following sequence of stages: obtaining(1510) person medical information, wherein the person medicalinformation is obtained by at least one out of (i) receiving personmedical information from the person and (iii) sensing person medicalinformation; detecting (1520) person location information indicative oflocations of the person during multiple points in time; sending (1530)the person location information and the person medical information to acomputerized system; receiving (1540) from the computerized system aperson infection alert that is generated in response to person locationinformation gathered from multiple persons and in response to medicalinformation; person medical information from at least one person; andinforming (1550) the person that the person is suspected as beinginfected by the infectious disease.

FIG. 16 illustrates method 1600 according to an embodiment of theinvention.

Method 1600 may include the following sequence of stages: receiving(1610), by a computerized system, (a) location information relating tolocations of multiple persons within a certain period of time; (b)medical information related to at least one person of the multiplepersons or (b) at least one of the locations; and (c) environmentalinformation relating to at least one of the locations; and evaluating(1620), by the computerized system, probabilities of infections of themultiple persons by at least one infectious disease, in response to thelocation information, to the medical information and to theenvironmental information.

The following examples include some tables that can be used in variousmethods mentioned above. It is noted that other data structures can beused.

FIG. 19 illustrates computerized system 20, multiple mobile devices 10,environmental sensors 12 and medical information source (such as ahospital) that is arranged to provide clinical information.

The mobile devices 10 can include sensors 12 and may host an applicationsuch as VT application 11.

The computerized system 20 can execute any of the methods mentionedabove and may include various modules such as infection signaturecalculator 22 for calculating signatures of infectious diseases,location information processor 28 for calculating location information,distances between users and the like, the calculation may be responsivefrom location information provided from the user devices, clinicalsymptoms information processor 32, medical information module 24 forretrieving and processing medical information, probability calculator 26for calculating probability attributes and any other statisticalmetadata, alert module 30 for generating alerts to specific users, tomultiple users and to third parties.

According to an embodiment of the invention objects and/or places may becauses of infection. That is, when the proposal refers to a “person” itmay also refer to an “object” or “location” which are suspect of beinginfectious. An example of an infectious object is a device used in ahospital (and is applicable mainly, if not only, to hospitals). Anexample of an infectious location may be a place of food (restaurantetc.) that is associated with people showing signs of infection aftervisiting the location, not necessarily at time when displaying symptoms.This way a contaminated food store may be traced for example. Again, itmay be more relevant to hospitals, where a room may be contaminated forlong periods of time and serve as a critical part of the infectionchain—for example, a contaminated WC room in a hospital.

People infected may be infective without showing signs of disease (i.e.,because of disease incubation period, because disease is stillinfectious after symptoms have ceased or because the disease isa-symptomatic (or nearly a-symptomatic) in the person, which may bequite common to some diseases. Thus the system may also “scan” forpotential a-symptomatic carriers that spread the disease. These personsare treated quite similarly to infected locations, since neither showssymptoms . . . . The main difference is that these people are mobile,and thus instead of trying to correlate infection to a particularlocation the system may attempt to associate an a-symptomatic person'sinteraction to the symptoms he has come in contact with. For example, ifI was a-symptomatic and met 5 people, 3 of which became infected not viaany obvious infection chain, the system may mark me as an “a-symptomaticcarrier” and notify me (or not).

The invention may also be implemented in a computer program for runningon a computer system, at least including code portions for performingsteps of a method according to the invention when run on a programmableapparatus, such as a computer system or enabling a programmableapparatus to perform functions of a device or system according to theinvention.

A computer program is a list of instructions such as a particularapplication program and/or an operating system. The computer program mayfor instance include one or more of: a subroutine, a function, aprocedure, an object method, an object implementation, an executableapplication, an applet, a servlet, a source code, an object code, ashared library/dynamic load library and/or other sequence ofinstructions designed for execution on a computer system.

The computer program may be stored internally on a non-transitorycomputer readable medium. All or some of the computer program may beprovided on computer readable media permanently, removably or remotelycoupled to an information processing system. The computer readable mediamay include, for example and without limitation, any number of thefollowing: magnetic storage media including disk and tape storage media;optical storage media such as compact disk media (e.g., CD-ROM, CD-R,etc.) and digital video disk storage media; nonvolatile memory storagemedia including semiconductor-based memory units such as FLASH memory,EEPROM, EPROM, ROM; ferromagnetic digital memories; MRAM; volatilestorage media including registers, buffers or caches, main memory, RAM,etc.

A computer process typically includes an executing (running) program orportion of a program, current program values and state information, andthe resources used by the operating system to manage the execution ofthe process. An operating system (OS) is the software that manages thesharing of the resources of a computer and provides programmers with aninterface used to access those resources. An operating system processessystem data and user input, and responds by allocating and managingtasks and internal system resources as a service to users and programsof the system.

The computer system may for instance include at least one processingunit, associated memory and a number of input/output (I/O) devices. Whenexecuting the computer program, the computer system processesinformation according to the computer program and produces resultantoutput information via I/O devices.

In the foregoing specification, the invention has been described withreference to specific examples of embodiments of the invention. It will,however, be evident that various modifications and changes may be madetherein without departing from the broader spirit and scope of theinvention as set forth in the appended claims.

Moreover, the terms “front,” “back,” “top,” “bottom,” “over,” “under”and the like in the description and in the claims, if any, are used fordescriptive purposes and not necessarily for describing permanentrelative positions. It is understood that the terms so used areinterchangeable under appropriate circumstances such that theembodiments of the invention described herein are, for example, capableof operation in other orientations than those illustrated or otherwisedescribed herein.

The connections as discussed herein may be any type of connectionsuitable to transfer signals from or to the respective nodes, units ordevices, for example via intermediate devices. Accordingly, unlessimplied or stated otherwise, the connections may for example be directconnections or indirect connections. The connections may be illustratedor described in reference to being a single connection, a plurality ofconnections, unidirectional connections, or bidirectional connections.However, different embodiments may vary the implementation of theconnections. For example, separate unidirectional connections may beused rather than bidirectional connections and vice versa. Also,plurality of connections may be replaced with a single connection thattransfers multiple signals serially or in a time multiplexed manner.Likewise, single connections carrying multiple signals may be separatedout into various different connections carrying subsets of thesesignals. Therefore, many options exist for transferring signals.

Although specific conductivity types or polarity of potentials have beendescribed in the examples, it will appreciated that conductivity typesand polarities of potentials may be reversed.

Each signal described herein may be designed as positive or negativelogic. In the case of a negative logic signal, the signal is active lowwhere the logically true state corresponds to a logic level zero. In thecase of a positive logic signal, the signal is active high where thelogically true state corresponds to a logic level one. Note that any ofthe signals described herein can be designed as either negative orpositive logic signals. Therefore, in alternate embodiments, thosesignals described as positive logic signals may be implemented asnegative logic signals, and those signals described as negative logicsignals may be implemented as positive logic signals.

Furthermore, the terms “assert” or “set” and “negate” (or “deassert” or“clear”) are used herein when referring to the rendering of a signal,status bit, or similar apparatus into its logically true or logicallyfalse state, respectively. If the logically true state is a logic levelone, the logically false state is a logic level zero. And if thelogically true state is a logic level zero, the logically false state isa logic level one.

Those skilled in the art will recognize that the boundaries betweenlogic blocks are merely illustrative and that alternative embodimentsmay merge logic blocks or circuit elements or impose an alternatedecomposition of functionality upon various logic blocks or circuitelements. Thus, it is to be understood that the architectures depictedherein are merely exemplary, and that in fact many other architecturescan be implemented which achieve the same functionality.

Any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality can be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated can also be viewed as being “operably connected,” or“operably coupled,” to each other to achieve the desired functionality.

Furthermore, those skilled in the art will recognize that boundariesbetween the above described operations merely illustrative. The multipleoperations may be combined into a single operation, a single operationmay be distributed in additional operations and operations may beexecuted at least partially overlapping in time. Moreover, alternativeembodiments may include multiple instances of a particular operation,and the order of operations may be altered in various other embodiments.

Also for example, in one embodiment, the illustrated examples may beimplemented as circuitry located on a single integrated circuit orwithin a same device. Alternatively, the examples may be implemented asany number of separate integrated circuits or separate devicesinterconnected with each other in a suitable manner.

Also for example, the examples, or portions thereof, may implemented assoft or code representations of physical circuitry or of logicalrepresentations convertible into physical circuitry, such as in ahardware description language of any appropriate type.

Also, the invention is not limited to physical devices or unitsimplemented in non-programmable hardware but can also be applied inprogrammable devices or units able to perform the desired devicefunctions by operating in accordance with suitable program code, such asmainframes, minicomputers, servers, workstations, personal computers,notepads, personal digital assistants, electronic games, automotive andother embedded systems, cell phones and various other wireless devices,commonly denoted in this application as ‘computer systems’.

However, other modifications, variations and alternatives are alsopossible. The specifications and drawings are, accordingly, to beregarded in an illustrative rather than in a restrictive sense.

In the claims, any reference signs placed between parentheses shall notbe construed as limiting the claim. The word ‘comprising’ does notexclude the presence of other elements or steps then those listed in aclaim. Furthermore, the terms “a” or “an,” as used herein, are definedas one or more than one. Also, the use of introductory phrases such as“at least one” and “one or more” in the claims should not be construedto imply that the introduction of another claim element by theindefinite articles “a” or “an” limits any particular claim containingsuch introduced claim element to inventions containing only one suchelement, even when the same claim includes the introductory phrases “oneor more” or “at least one” and indefinite articles such as “a” or “an.”The same holds true for the use of definite articles. Unless statedotherwise, terms such as “first” and “second” are used to arbitrarilydistinguish between the elements such terms describe. Thus, these termsare not necessarily intended to indicate temporal or otherprioritization of such elements. The mere fact that certain measures arerecited in mutually different claims does not indicate that acombination of these measures cannot be used to advantage.

While certain features of the invention have been illustrated anddescribed herein, many modifications, substitutions, changes, andequivalents will now occur to those of ordinary skill in the art. It is,therefore, to be understood that the appended claims are intended tocover all such modifications and changes as fall within the true spiritof the invention.

We claim:
 1. A non-transitory computer readable medium that storesinstructions for causing a computerized system to perform the followingoperations: determining, by the computerized system, that a first personis infected by a first infectious disease; wherein the determination isassociated with a first person infection probability attribute;detecting, by the computerized system, based upon location informationcollected during at least a portion of a first infectious diseasemanifestation period, the location information being indicative oflocations of the first person and other persons, a second person thatwas within an infection distance from the first person and ispotentially infected by the first infectious disease; calculating, bythe computerized system, a second person infection probabilityattribute; and updating, by the computerized system, the first personinfection probability attribute in response to the second personinfection probability attribute; wherein at least one of the followingis being held true: (i) the determining is responsive to at least one of(a) first person medical information fed by the first person, and (b)clinical information related to at least one of the first person and thefirst infectious disease; (ii) the determining is responsive tosignatures of multiple infectious diseases; (iii) the locationinformation is collected from hand held devices of the persons; (iv) thelocation information is collected from short-range transmission devicescarried by the persons; (v) the determining and the calculating areresponsive to demographic information relating to the first and secondpersons and to a demographic element of the signature of the firstinfectious disease; (vi) the determining and the calculating areresponsive to demographic information relating to the first and secondpersons and to a demographic element of the signature of the firstinfectious disease and the non-transitory computer readable mediumfurther stores instructions for updating, by the computerized system,the first person infection probability attribute and the second personinfection probability attribute in response to clinical symptomsinformation relating to at least one of the first person, second personand the first infectious disease.
 2. The non-transitory computerreadable medium according to claim 1, wherein the determining isresponsive to at least one of (a) first person medical information fedby the first person, and (b) clinical information related to at leastone of the first person and the first infectious disease.
 3. Thenon-transitory computer readable medium according to claim 1, whereinthe determining is responsive to signatures of multiple infectiousdiseases.
 4. The non-transitory computer readable medium according toclaim 1, wherein the location information is collected from hand helddevices of the persons.
 5. The non-transitory computer readable mediumaccording to claim 4, wherein the location information is collected fromshort-range transmission devices carried by the persons.
 6. Thenon-transitory computer readable medium according to claim 4, whereinthe location information is collected from long-range transmissiondevices carried by the persons.
 7. The non-transitory computer readablemedium according to claim 1, wherein the determining and the calculatingare responsive to demographic information relating to the first andsecond persons and to the demographic element of the signature of thefirst infectious disease.
 8. The non-transitory computer readable mediumaccording to claim 1 that stores instructions for updating, by thecomputerized system, the first person infection probability attributeand the second person infection probability attribute in response toclinical symptoms information relating to at least one of the firstperson, second person and the first infectious disease.
 9. Anon-transitory computer readable medium that stores instructions forcausing a computerized system to perform the following operations:receiving, by a computerized system, (a) location information relatingto locations of multiple persons within a certain period of time; (b)clinical symptoms information related to one or more out of (a) at leastone person of the multiple persons or (b) at least one of the locations;evaluating, by the computerized system, probabilities of infections ofthe multiple persons by at least one infectious disease, in response tothe location information and to the clinical symptoms information; andwherein at least one of the following is being held true: (i) thenon-transitory computer readable medium further stores instruction forgenerating a person infection alert if a probability of infection of theperson by an infectious disease exceeds a first threshold; and fortransmitting the person infection alert to at least the person; (ii) thenon-transitory computer readable medium further stores instruction forgenerating a geographical zone infection alert if probabilities of atleast a predefined number of persons that were, during the certainperiod of time, within the geographical infection alert exceed a secondthreshold; and for transmitting the geographical zone infection alert toa plurality of persons; (iii) the non-transitory computer readablemedium further stores instruction for calculating infectious diseasesignatures in response to the location information and to the clinicalsymptoms information; (iv) the non-transitory computer readable mediumfurther stores instruction for receiving by the computerized system,medical information provided by the persons; and wherein the evaluating,by the computerized system, of the probabilities of infections of themultiple persons is further responsive to medical information providedby the persons; (v) the non-transitory computer readable medium furtherstores instruction for receiving by the computerized system, medicalinformation sensed by mobile devices of the multiple persons; andwherein the evaluating, by the computerized system, of the probabilitiesof infections of the multiple persons is further responsive to medicalinformation provided by the persons; (vi) the non-transitory computerreadable medium further stores instruction for receiving voiceinformation sensed by mobile devices of the multiple persons; andprocessing the voice information to detect disease related voices; (vii)the non-transitory computer readable medium further stores instructionfor calculating, by the computerized system, infections diseasetransmission pathways based upon the location information and theclinical symptoms information.
 10. The non-transitory computer readablemedium according to claim 9 that stores instructions for generating theperson infection alert if the probability of infection of the person bythe infectious disease exceeds the first threshold; and for transmittingthe person infection alert to at least the person.
 11. Thenon-transitory computer readable medium according to claim 9 that storesinstructions for generating the geographical zone infection alert ifprobabilities of at least the predefined number of persons that were,during the certain period of time, within the geographical infectionalert exceed the second threshold; and for transmitting the geographicalzone infection alert to the plurality of persons.
 12. The non-transitorycomputer readable medium according to claim 9 that stores instructionsfor calculating infectious disease signatures in response to thelocation information and to the clinical symptoms information.
 13. Thenon-transitory computer readable medium according to claim 9 that storesinstructions for receiving by the computerized system, medicalinformation provided by the persons; and wherein the evaluating, by thecomputerized system, of the probabilities of infections of the multiplepersons is further responsive to medical information provided by thepersons.
 14. The non-transitory computer readable medium according toclaim 9 that stores instructions for receiving by the computerizedsystem, medical information sensed by mobile devices of the multiplepersons; and wherein the evaluating, by the computerized system, of theprobabilities of infections of the multiple persons is furtherresponsive to medical information provided by the persons.
 15. Thenon-transitory computer readable medium according to claim 9 that storesinstructions for receiving voice information sensed by mobile devices ofthe multiple persons; and processing the voice information to detectdisease related voices.
 16. The non-transitory computer readable mediumaccording to claim 9 that stores instructions for calculating, by thecomputerized system, infections disease transmission pathways based uponthe location information and the clinical symptoms information.
 17. Anon-transitory computer readable medium that stores instructions forcausing a computerized system to perform the following operations:receiving, by a computerized system, (a) location information relatingto locations of multiple persons at different points of time; and (b)medical information that comprises at least one of (i) medicalinformation fed by at least one person; (ii) medical information sensedby at least one mobile device of at least one person; (iii) clinicalsymptoms information related to at least one person; and (iv) medicalsymptoms information related to at least one of the locations;calculating by the computerized system and in response to the locationinformation and to the medical information, infectious diseasessignatures that comprise spatial and temporal characteristics of adistribution of the infections diseases; and wherein at least one of thefollowing is being held true: i) the non-transitory computer readablemedium further stores instruction for evaluating, by the computerizedsystem, probabilities of infections of the multiple persons by at leastone infectious disease, in response to the location information and tothe, infectious disease signatures; ii) the non-transitory computerreadable medium further stores instruction for receiving by thecomputerized system demographic information related to at least one ofthe locations; and calculating by the computerized system and inresponse to the location information, the medical information and thedemographic information, infectious diseases signatures that comprisesenvironmental condition, spatial and temporal characteristics of thedistribution of the infections diseases; iii) the non-transitorycomputer readable medium further stores instruction for receiving by thecomputerized system environmental information related to the multiplelocations persons; and calculating by the computerized system and inresponse to the location information, the medical information and thedemographic information, infectious diseases signatures that comprisedemographic, spatial and temporal characteristics of the distribution ofthe infections diseases.
 18. The non-transitory computer readable mediumaccording to claim 17 that stores instructions for evaluating, by thecomputerized system, probabilities of infections of the multiple personsby at least one infectious disease, in response to the locationinformation and to the, infectious disease signatures.
 19. Thenon-transitory computer readable medium according to claim 17 thatstores instructions for receiving by the computerized system demographicinformation related to at least one of the locations; and calculating bythe computerized system and in response to the location information, themedical information and the demographic information, infectious diseasessignatures that comprises environmental condition, spatial and temporalcharacteristics of the distribution of the infections diseases.
 20. Thenon-transitory computer readable medium according to claim 17 thatstores instructions for receiving by the computerized systemenvironmental information related to the multiple locations persons; andcalculating by the computerized system and in response to the locationinformation, the medical information and the demographic information,infectious diseases signatures that comprise demographic, spatial andtemporal characteristics of the distribution of the infections diseases.